Implementing Machine Learning For Life And Health Insurance Claims Handling

ABSTRACT

Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network, A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user&#39;s mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority and the benefit of:

-   -   U.S. Application No. 62/564,055, filed Sep. 27, 2017 and        entitled “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. Application No. 62/580,655, filed Nov. 2, 2017 and entitled        REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING        DAMAGE AND OTHER CONDITIONS;”    -   U.S. Application No. 62/610,599, filed Dec. 27, 2017 and        entitled “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. Application No. 62/621,218, filed Jan. 24, 2018 and        entitled “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS        MITIGATION AND CLAIMS HANDLING;”    -   U.S. Application No. 62/621,797, filed Jan. 25, 2018 and        entitled “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS        RESERVING AND FINANCIAL, REPORTING;”    -   U.S. Application No. 62/580,713, filed Nov. 2, 2017 and entitled        “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING        DAMAGE AND OTHER CONDITIONS;”    -   U.S. Application No. 62/618,192, filed Jan. 17, 2018 and        entitled “REAL PROPERTY MONITORING SYSTEMS ANI) METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. Application No. 62/625,140, filed Feb. 1, 2018 and entitled        “SYSTEMS ANI) METHODS FOR ESTABLISHING LOSS RESERVES FOR        BUILDING/REAL PROPERTY INSURANCE;”    -   U.S. Application No. 62/646,729, filed Mar. 22, 2018 and        entitled “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR LOSS        MITIGATION AND CLAIMS HANDLING;”    -   U.S. Application No. 62/646,735, filed Mar. 22, 2018 and        entitled “REAL PROPERTY MONITORING SYSTEMS ANI) METHODS FOR RISK        DETERMINATION;”    -   U.S. Application No. 62/646,740, filed Mar. 22, 2018 and        entitled “SYSTEMS ANI) METHODS FOR ESTABLISHING LOSS RESERVES        FOR BUILDING/REAL PROPERTY INSURANCE;”    -   U.S. Application No. 62/617,851, filed Jan. 16, 2018 and        entitled “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH        INSURANCE PRICING AND UNDERWRITING,”    -   U.S. Application No. 62/622,542, filed Jan. 26, 2018 and        entitled “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH        INSURANCE LOSS MITIGATION AND CLAIMS HANDLING;”    -   U.S. Application No. 62/632,884, filed Feb. 20, 2018 and        entitled “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH        INSURANCE LOSS RESERVING ANI) FINANCIAL REPORTING;”    -   U.S. Application No. 62/652,121, filed Apr. 3, 2018 and entitled        “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE        CLAIMS HANDLING;”

the entire disclosures of which are hereby incorporated by referenceherein in their entireties.

FIELD OF INVENTION

This disclosure generally relates to implementing machine learning toautomate aspects of life, worker's compensation, disability, and/orhealth insurance claims processing and, more particularly, to improveupon the customer claims handling experience by processing claims usingmachine learning techniques.

BACKGROUND

Historically, a claim relating to a life, worker's compensation,disability, and/or health insurance policy may be reported to an issuerof the insurance policy (e.g., an insurance company) upon the occurrenceof an event covered under the policy. The claim may be allocated to aclaims examiner who may manage the claim. For example, the claimsexaminer may manually update paper and/or electronic files related tothe reported, or filed, claim as claim information is provided by theclaimant, and/or collected by the insurer. The claims examiner mayconduct an investigation and may contact the policy holder/claimantand/or others (e.g., beneficiaries, witnesses, government employees,third parties, etc.).

In many instances, the claim handling process may include time-consumingand fact-intensive processes and procedures. However, insurers aremotivated to timely investigate and pay claims promptly. At the sametime, insurers are also motivated to identify fraudulent claims orbuildup, so as to not penalize all customers with higher rates.

BRIEF SUMMARY

The present disclosure generally relates to methods and systems forimplementing machine learning to improve upon aspects of life, worker'scompensation, disability, and/or health insurance claim processing andhandling throughout the claims lifecycle. In some embodiments, neuralnetworks may be used. Other machine learning techniques, including thosediscussed elsewhere herein may also be employed. Embodiments ofexemplary systems and computer-implemented methods are described below.

In one aspect, a computer-implemented method of automated claimshandling may include (1) receiving a set of labeled historical claims(including life, worker's compensation, disability, and/or healthclaims), each one corresponding to a respective adjusted settlementamount. The method may include (2) training an artificial neural networkusing a subset of the labeled historical claims and/or each respectiveadjusted settlement amount. The method may then include (3) receiving alife, worker's compensation, disability, and/or health claim from auser, such as from their mobile device; (4) analyzing the life, worker'scompensation, disability, and/or health claim using the trainedartificial neural network to determine a claim settlement prediction;and (5) generating, based upon the settlement prediction, a settlementoffer. The method may also include (6) transmitting the settlement offerto an application in a user mobile or other device, such as via wirelesscommunication or data transmission. The method may include additional,less, or alternate actions.

In another aspect, a claim handling system may include one or moreprocessors and one or more memories storing instructions. When theinstructions are executed by the one or more processors, they may causethe claim handling system to (1) receive a set of life or health claiminformation from the user; (2) predict a claim settlement amount byanalyzing the set of life or health claim information using a trainedartificial network; (3) generate a settlement offer based upon the claimsettlement amount; (4) display the settlement offer in the user device;and/or (5) receive a manifestation of acceptance from the user device.The system may include additional, less, or alternate functionality,including that discussed elsewhere herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals. The figures shown are simplified forexpository purposes, and the present embodiments are not limited to theprecise arrangements and instrumentalities shown and discussed.

FIG. 1 depicts an exemplary computing environment implementing machinelearning to improve upon aspects of life and health insurance claimhandling;

FIG. 2 depicts an exemplary computing environment implementingcollection and processing of user input and machine learningimplementing artificial intelligence techniques for life and healthinsurance claim handling;

FIG. 3 depicts an exemplary neural network which may be trained andoperated by the neural network unit of FIG. 1 or the neural networktraining application of FIG. 2 , according to one embodiment andscenario;

FIG. 4 depicts an exemplary neuron, which may be included in the neuralnetwork of FIG. 3 , according to one embodiment and scenario;

FIG. 5 depicts content of an exemplary electronic life claim record thatmay be processed by an artificial neural network, in one embodiment; and

FIG. 6 depicts a flow diagram of an exemplary computer-implementedmethod of training an artificial neural network to handle insuranceclaims, according to one embodiment.

The figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION Artificial Intelligence System for Life ClaimsProcessing

The present embodiments are directed to, inter alia, training andoperating a machine learning model to analyze historical life insuranceand/or health insurance claims to, in turn, handle life insurance and/orhealth insurance claims. Herein a “life claim” may relate to a life,worker's compensation, and/or disability insurance claim. Each lifeclaim may relate to one or more insurance policies. Systems and methodsmay include natural language processing of free-form notes/text, and/orfree-form speech/audio, recorded by a call center and/or a claimadjustor. Photographic evidence and/or other evidence may be used. Allsuitable input may be received from a customer speaking into a mobiledevice app that records the customer's speech, and/or into a chat bot orrobo-advisor. Handling of a claim may include settling a claim in fulland/or in part, flagging the claim for human review, requestingadditional information, etc.

The methods and systems may analyze data harvested from subsets ofhistorical claims to train and validate machine learning (ML) models,and to analyze filed claims by executing the trained and validatedmodels providing aspects of the filed claims as input to the trained MLmodels. The methods and systems may access electronic data and may groupand/or classify claim submissions by claim type prior to analysis by oneor more trained ML model. For instance, the methods and systems maycategorize a claim submission as a life and/or health insurance policy.The electronic data may include information about the insured including,without limitation: electronic medical records, demographic information,insurance records, lifestyle information, psychographic information,etc. For example, the trained ML and/or artificial neural network modelmay accept as input the insured's age, medical history includingelectronic medical records, location, etc. Information pertaining to anaccident and/or event may be input into and analyzed by the trained NILmodel. The methods and systems may collect electronic data to constructa dynamic data set, which may change over time as additional informationis collected and/or as additional users contribute to an overall datapool that includes a plurality of electronically accessible data eachrelating to a customer's claims.

The methods and systems may use the dynamic data set to train one ormore machine-learning analytics models in an incremental or “online”fashion, so that a model need not be entirely retrained from scratchwhen new information is received (e.g., when a claimant submitssupplemental documentation). Retraining may be skipped in some cases,such as when a pre-set level of model accuracy has already beenattained. For example, when a new claim is received from a claimant orthird party, the methods and systems may check model accuracy, and ifthe accuracy is above 99.9%, or another appropriate threshold, theadditional training may be avoided.

Generally, the methods and systems may analyze electronic claim data inaccordance with the one or more trained machine-learning analyticsmodels to automatically process insurance claims, wherein processing theclaim may include any actions suitable to the particular claim type.Traditionally, such actions may have been historically performedmanually by an insurance claims intake processor and/or adjustor. Forexample, a claim may be categorized by type and funneled to a particulargroup, unit, and/or department of claim adjustors focusing on aparticular claim type (e.g., “life insurance,” “health insurance,”“casualty,” etc.).

The present methods and systems may include means for automaticallyassigning the claim to one or more of the adjustors within a department,wherein the assignment may be based upon an analysis of the claim todetermine its category, severity and/or amount of time estimated tocomplete one or more stages of processing the claim. The assignment ofthe claim to a department or adjustor may be allocated according to theseniority, experience level, and/or existing backlog of the claimsadjustor. It should be appreciated that in one embodiment, theclassification step may be omitted, for example, wherein anothercomponent validates or forces a categorization.

After the claim is classified, the trained ML model may analyze theclaim to determine a number of details. For example, in the lifeinsurance example, the ML model or another component may attempt toextract a death certificate submitted with the claim. The ML model mayfurther attempt to validate the death certificate via internalvalidation (e.g., by reference to a database or validation mechanismowned by the corporation and/or an independently-trainedmachine-learning model) or external validation (e.g., agovernment-operated API or a privately-operated API). The validationmethods may determine a provisional or final authenticity and/orvalidity of the death certificate, which may be a necessary preconditionto additional processing of the claim (e.g., before the insurer pays alife insurance claim). A specific model may be trained for validationtasks, and an authenticity output of a trained ML model may be a booleanvalue (e.g., TRUE or FALSE) or a scaled value (e.g., a percentage)representing a probability or likelihood that the death certificate isauthentic. In some cases, a confirmation number or hash value may bestored in association with a validation result obtained via an externaldatabase.

The validation steps for health insurance claims may differ from thosefor life insurance, and may be based upon an entirely different set ofanalyses, different training, and/or an alternate and/or additionaltrained model. For example, a validation system and method relating tohealth insurance may include associating a doctor, hospital, clinic, orother patient visit to a particular claim. This association may beperformed by an ML model determining patient visit information, such asa date of service, type of service, description of procedure, dollaramount, etc.

In one embodiment, a ML model may be trained that analyzes a claim and,based upon information related to the patient or a subset of similarpatients, determines whether a claim is payable without human reviewbased upon similar patients and/or past treatment of the patient.Similar patients may be identified by training a MI, model using patientinformation corresponding to patients in a similar geographic region(e.g., in a city or region of a state). Similar patients may beidentified by identifying a cost-of-living index or geospatial search.

For example, a patient may have hand surgery performed in Bloomington,Ill. at a particular hospital, by a particular surgeon. The surgery maylast for one half hour, cost $10,000, and the description of the surgerymay include the term “carpal tunnel.” Each of the foregoing facts (i.e.,the type of surgery, locus within the body, location of the patient,hospital name, surgeon identity, cost, duration, description, etc.) andmore may be used to train ML models using a database of hand surgeryclaims as training data, wherein those facts from each respective handsurgery claims are analyzed in training. Using these inputs, an ML modelmay be trained to predict whether a given claim for a hand surgeryshould be paid or not, and if so, a settlement amount or amounts.

Health insurance claims may be determined to be not immediately payableand in need of further review due to a number of factors, some of whichmay not be immediately apparent based upon a predictive output of atrained ML model. For example, the duration of the surgery may be longerthan normal. The cost reported by the medical facility may be aberrantlyhigh, or aberrantly low. The person listed as having performed a surgery(e.g., a hand surgery) may have left the practice of medicine, may havebeen suspended from practice and/or had her license revoked, may not beboard-certified, and/or may not be engaged in orthopedic practice. Suchinformation pertaining to the qualifications of physicians may bediscoverable by reference to third-party data sources. Although thepresence of one or more factors may cause a payable or non-payableoutcome to be produced by the trained ML model, the factors may or maynot be particularly identified. In one embodiment, the trained ML modelmay merely output an indication that the claim is payable ornon-payable, and that human review is necessary.

It should be appreciated that the ML model may have an unlimited numberof inputs, and that some inputs may include procedural informationrelated to claim processing, such as whether a patient has submitted asigned informed consent form, or the length of time elapsed between thecarrying out of a medical procedure and the submission of a claim forthe procedure. Other factors may be common to all ML models. Forexample, a single ML model may determine whether a request for benefitscontains an ink signature. To the extent that a claim requires a requestfor benefits, the ML model may “short-circuit” the claim handlingprocess by requiring that the request for benefits carry the inksignature of the claimant(s) before the claim may be paid and/oranalyzed further for settlement purposes. One of the benefits of thecomputerized methods and systems disclosed herein is that, during normaloperation, they may create a time-stamped paper trail that may be usedby the insurer to rebut any allegation on the part of the claimant thatthe insurer has unduly delayed the handling of the claim. The methodsand systems may also be more reliable than any relying on human review.

An artificial intelligence platform including an ML model and/orartificial neural network may react based upon certain inputs. Forexample, some life insurance policies may include contestabilityclauses, which allow the insurance company to contest payment under apolicy under certain circumstances. For example, if a policy holder'scause of death is listed as a suicide, and the effective date of thepolicy is less than two years from the present time, then the methodsand systems herein may automatically cause the policy to be considerednon-payable pending further investigation.

In another example, a cause of death of homicide may require furtherscrutiny and/or investigation of a beneficiary. For example, if abeneficiary is charged in relation to a homicide death, thencertification of acquittal or conviction may be required before a claimmay be payable. The methods and systems described herein may includeinstructions for automatically detecting certain causes of death, fordetermining payable status at a later time, or after the occurrence of acondition (e.g., by reference to court/public records), and fornotifying a claimant or other party of a contested payment (e.g., viaemail or another electronic notification).

In some embodiments, the methods and systems herein may include thecreation of ML models that are intended to ensure an optimal outcome forbeneficiaries based upon information about the beneficiary. For example,a death benefit of $5,000,000 may be payable to a beneficiary. A trainedML model may analyze the income and/or dependency status of thebeneficiary and determine that the beneficiary has an income of $50,000and three dependents aged 5, 8, and 13. The ages of the beneficiary anddependents may be provided to a trained ML model, which may predict thesuitability of (1) a lump sum payment, and/or (2) a series ofinstallment/annuity payments.

As noted, in many cases, a ML model may be trained to provideaccelerated death benefits to beneficiaries. Such models may includeadditional training geared to providing terminally ill patients withadditional funds providing for “right to try” (e.g., off-label)prescriptions, homeopathic remedies, palliative/hospice care, and othertherapies. In some embodiments, an ML model may provide terminally illpatients with quality of life funds that are non-therapeutic in nature(e.g., make-a-wish, or “bucket list” funds).

In some embodiments, a specific model may be trained for analyzingclaims pertaining to terminal, chronic, and/or critical illnesses. Thus,in some embodiments, it should be apparent that a claimant andbeneficiary may be one and the same. For example, a ML model may betrained to handle claims submitted by patients diagnosed with terminalcancer. A ML model may be trained to handle claims of patients diagnosedwith non-terminal forms of cancer, and/or other non-fatal illnesses. Thedetermination of which model(s) to use to process a patient's claims maybe based upon the methods and systems verifying a diagnosis andclassifying the patient on the basis of such verification.

Certain diagnoses may cause some models to supersede other models. Forexample, claims submitted by a patient diagnosed with gout andnon-terminal cancer may be handled by a non-terminal cancer ML modeleither prior to, in addition to, or instead of, an ML model handling thepatient's gout-related claims. It should be appreciated that the numberof potential trained ML models is only limited by the number ofdiagnoses and sub-combinations thereof.

In one embodiment, a ML model may predict a cause of death of a patientbased upon training on prior claims data. In this way, a conclusion of acause of death reached or predicted by a trained ML model may beconfirmed by, or in conflict with, an official cause of death. In suchcases, further investigation may be required before a death benefit willbe paid or a claim settled. In some embodiments, a trained ML model mayidentify a trust (e.g., a revocable trust) beneficiary of an insurancebenefit, and may also provide information relating to taxation of deathbenefits. For example, the system may communicate with the trustee of atrust, a beneficiary, and/or another responsible party such as a legalagent/representative.

Exemplary Model Training Environment

FIG. 1 illustrates a block diagram of an exemplary computing environment100 implementing machine-learning for insurance claim handling, inaccordance with certain aspects of the present disclosure. Thehigh-level architecture includes both hardware and softwareapplications, as well as various data communication channels forcommunicating data between the various hardware and software components.Generally, the environment 100 may automatically retrieve dataassociated with various electronic records, data sources, and/or users(e.g., claimants and/or beneficiaries) and use this data set toimplement the various machine-learning implementations discussed hereinto facilitate improvements to the insurance claim handling process.

In the present aspect, the computing environment 100 may include aninput data set 102, an artificial intelligence (AI) platform 104, asettlement offer 106, and an historical data 108. Input data set 102 andhistorical data 108 may include a plurality (e.g., thousands ormillions) of electronic documents, parameters, and/or other information.As used herein, the term “data” generally refers to information relatedto a policy holder, which may exist in the environment 100. For example,data may include an electronic document representing an insurancepolicy, an insurance claim, demographic information about the policyholder, and/or information related to the type of insurance claimsubmitted. Examples of insurance policy data may include, withoutlimitation: a term, begin date, death benefit, and deductible. Data maybe historical or current. Although data may be related to an ongoingclaim filed by a policy holder or beneficiary, in some embodiments, datamay consist of raw data parameters entered by a human user of theenvironment 100 or which is retrieved/received from another computingsystem.

Data may or may not relate to the claims filing process, and while someof the examples described herein refer to health and life insuranceclaims, it should be appreciated that the techniques described hereinmay be applicable to other types of electronic documents, in otherdomains. For example, the techniques herein may be applicable toidentifying risk factors in other insurance domains, such asagricultural insurance, homeowners insurance, vehicle insurance, rentersinsurance, etc. In that case, the scope and content of the data maydiffer, as may the techniques used for training and operating themachine learning models. As another example, data may be collected froman existing customer filing a claim, a potential or prospective customerapplying for an insurance policy, and/or may be supplied by a thirdparty such as a company other than the proprietor of the environment100. In some cases, data may reside in paper files that are scanned orentered into a digital format by a human or by an automated process(e.g., via a scanner). Generally, data may comprise any digitalinformation, from any source, created at any time.

Input data 102 may be loaded into an artificial intelligence system 104to organize, analyze, and process input data 102 in a manner thatfacilitates claim handling by AI platform 104. The loading of input data102 may be performed by executing a computer program on a computingdevice that has access to the environment 100, and the loading processmay include the computer program coordinating data transfer betweeninput data 102 and AI platform 104 (e.g., by the computer programproviding an instruction to AI platform 104 as to an address or locationat which input data 102 is stored).

AT platform 104 may reference this address to retrieve records frominput data 102 to perform claim handling techniques. AI platform 104 maybe thought of as a collection of algorithms and/or rules configured toreceive and process parameters, and to produce labels and, in someembodiments, quantify claim settlement information. AI platform 104 maybe used to process claim data inputs, train and operate multipleartificial neural networks (ANNs), and generate settlement offers basedupon claim quantification. Herein, an ANN may be defined and/orconstructed as an ANN, and/or as another artificial intelligence ormachine learning algorithm, program, module, and/or model.

AI platform 104 may include an input analysis unit 120, which mayinclude a speech-to-text unit 122 and an image processing unit 124.These may comprise, respectively, instructions for converting humanspeech into text and analyzing images (e.g., extracting information froma death certificate or other legal document). In this way, data maycomprise audio recordings (e.g., recordings made when a customertelephones a customer service center) that may be converted to text andfurther used by AI platform 104. In some embodiments, customer behaviorrepresented in data—including the accuracy and truthfulness of acustomer—may be encoded by claim analysis unit 120, and used by AIplatform 104 to train and operate ANN models.

Input analysis unit 120 may also include text analysis unit 126, whichmay include pattern matching unit 128 and natural language processing(NLP) unit 130, In some embodiments, text analysis unit 126 maydetermine facts regarding claim inputs (e.g., the amount of money paidunder a claim). Amounts may be determined in a currency—andinflation—neutral manner, so that claim loss amounts may be directlycompared. In some embodiments, text analysis unit 126 may analyze textproduced by speech-to-text unit 122 or image analysis unit 124.

In some embodiments, pattern matching unit 128 may search textual claimdata loaded into AI platform 104 for specific strings or keywords intext (e.g., “cardiac arrest”) which may be indicative of a condition.NLP unit 130 may be used to identify, for example, entities or objectsindicative of a fact (e.g., that an individual visited an emergencyroom). NLP unit 130 may identify human speech patterns in data,including semantic information relating to entities, such as people,buildings, businesses, etc.

Relevant verbs and objects, as opposed to verbs and objects of lesserrelevance, may be determined by the use of a machine learning algorithmanalyzing historical claims. For example, verbs indicating injury orsurgery may be relevant verbs. In some embodiments, text analysis unit126 may comprise text processing algorithms/techniques (e.g., lexers andparsers, regular expressions, etc.) and may emit structured text in aformat which may be consumed by other components.

In the embodiment of FIG. 1 , AI platform 104 may include a lossclassifier 110 to classify losses by category. As discussed above,losses may be categorized as a first step according to loss type (e.g.,a health insurance claim or life insurance claim). For example, lossclassifier 140 may label input data 102, or portions thereof, accordingto positive or negative pattern matching according to pattern matchingunit 128 and/or natural language processing unit 130. For example, ifinput data 102 includes data matching the pattern “asthma attack” andsemantic information indicating that a person visited a hospital and wasprescribed a nebulizer, then loss classifier 140 may classify the claimdata as a health insurance claim, and the processing of the claim mayproceed accordingly (e.g., by the claim data being processed in ANN unit150 by a health insurance-specific ANN model).

ANN unit 150 may process claim data by training models with the dataand/or by operating a trained ANN model with the data. ANN unit 150 mayuse an ANN or another suitable model, as described above. The ANN may beany suitable type of ANN, including, without limitation, a recurrentneural network or feed-forward neural network. The ANN may include anynumber (e.g., thousands) of nodes or “neurons” arranged in multiplelayers, with each neuron processing one or more inputs to generate adecision or other output. In other embodiments, other types ofartificial intelligence or machine learning algorithms, programs,modules, or models may be used.

In some embodiments, ANN models may be chained together, so that outputfrom one model is fed into another model as input. For example, lossclassifier 140 may, in one embodiment, apply input data 102 to a firstANN model that is trained to generate labels. The output (e.g., labels)of this first ANN model may be fed as input to a second ANN model whichhas been trained to predict claim settlement amounts based upon thepresence of labels. The second ANN may be trained using aninflation-adjusted set of claim payout amounts, and respective set ofrisk labels, to very accurately predict the amount of money likely to bepaid on a new claim, given only a new set of risk labels from the firstmodel.

ANN unit 150 may include training unit 152, and risk indication unit154. To train the ANN to identify risk, ANN unit 150 may accesselectronic claims within historical data 108. Historical data 108 maycomprise a corpus of documents comprising many (e.g., millions) ofinsurance claims which may contain data linking a particular customer orclaimant to one or more vehicles, and which may also contain, or belinked to, information pertaining to the customer. In particular,historical data 108 may be analyzed by AI platform 104 to generate claimrecords 110-1 through 110-n, where n is any positive integer. Each claim110-1 through 110-n may be processed by training unit 152 to train oneor more ANNs to identify claim risk factors, including by pre-processingof historical data 108 using input analysis unit 120 as described above.

ANN 150 may, from a trained model, identify labels that correspond tospecific data, metadata, and/or attributes within input data 102,depending on the embodiment. For example, ANN 150 may be provided withinstructions from input analysis unit 120 indicating that one or moreparticular type of insurance is associated with one or more portions ofinput data 102.

ANN 150 may identify one or more insurance types associated with the oneor more portions of input data 102 (e.g., bodily injury, propertydamage, collision coverage, comprehensive coverage, liability insurance,med pay, or personal injury protection (PIP) insurance) and by inputanalysis unit 120. In one embodiment, the one or more insurance typesmay be identified by training the ANN 150 based upon types of peril. Forexample, the ANN model may be trained to determine that death ordiagnosis with a terminal illness may indicate life insurance coverage.

In addition, input data 102 may indicate a particular customer and/orbeneficiary. In that case, loss classifier 140 may look up additionalcustomer information from customer data 160 and life and health database162, respectively. For example, the age of the person and/or informationpertaining to the person's life insurance policy and/or health carebenefits may be obtained. The additional customer and/or life and healthinformation may be provided to ANN unit 150 and may be used to analyzeand label input data 102 and, ultimately, may be used to determinesettlement offer 106. For example, ANN unit 150 may be used to quantifya settlement amount based upon inputs obtained from a person submittinga life insurance claim, or based upon a claim submitted by a person whois a holder of an existing health insurance policy, That is, in someembodiments where ANN unit 150 is trained on claim data, ANN unit 150may quantify settlement amounts based upon raw information unrelated tothe claims filing process, or based upon other data obtained during thefiling of a claim (e.g., a claim record retrieved from historical data108).

In one embodiment, the training process may be performed in parallel,and training unit 152 may analyze all or a subset of claims 110-1through 110-n. Specifically, training unit 152 may train an ANN toidentify claim risk factors in claim records 110-1 through 110-n. Asnoted, AI platform 104 may analyze input data 102 to arrange thehistorical claims into claim records 110-1 through 110-n, where n is anypositive integer.

Claim records 110-1 through 110-n may be organized in a flat liststructure, in a hierarchical tree structure, or by means of any othersuitable data structure. For example, the claim records may be arrangedin a tree wherein each branch of the tree is representative of one ormore customer. There, each of claim records 110-1 through 110-n mayrepresent a single non-branching claim, or may represent multiple claimrecords arranged in a group or tree.

Further, claim records 110-1 through 110-n may comprise links tocustomers whose corresponding data is located elsewhere. In this way,one or more claims may be associated with one or more customers viaone-to-many and/or many-to-one relationships. Risk factors may be dataindicative of a particular risk or risks associated with a given claim,and/or customer. The status of claim records may be completely settledor in various stages of settlement.

As used herein, the term “claim” generally refers to an electronicdocument, record, or file, that represents an insurance claim (e.g., alife insurance or health insurance claim) submitted by a policy holder,beneficiary, and/or legal representative of an insurance company.Herein, “claim data” or “historical data” generally refers to datadirectly entered by the customer or insurance company including, withoutlimitation, free-form text notes, photographs, audio recordings, writtenrecords, receipts (e.g., doctor's visit invoices) and other informationincluding data from legacy, including pre-Internet (e.g., paper file)systems. Notes from claim adjustors and attorneys may also be included.

In one embodiment, claim data may include claim metadata or externaldata, which generally refers to data pertaining to the claim that may bederived from claim data or which otherwise describes, or is related to,the claim but may not be part of the electronic claim record. Claimmetadata may have been generated directly by a developer of theenvironment 100, for example, or may have been automatically generatedas a direct product or byproduct of a process carried out in environment100. For example, claim metadata may include a field indicating whethera claim was settled or not settled, and amount of any payouts, and theidentity of corresponding payees.

Another example of claim metadata is the geographic location in which aclaim is submitted, which may be obtained via a global positioningsystem (GPS) sensor in a device used by the person or entity submittingthe claim. Yet another example of claim metadata includes a category ofthe claim type (e.g., term life insurance, whole life, universal life,etc.). For example, a single claim in historical data 108 may beassociated with a married couple, and may include the name, address, andother demographic information relating to the couple. Additionally, theclaim may include an indication of beneficiaries corresponding to thecouple. The claim may include a plurality of claim data and claimmetadata, including metadata indicating a relationship or linkage toother claims in historical claim data 108.

Once the ANN has been trained, quantification unit 154 may apply thetrained ANN to input data 102 as processed by input analysis unit 120.In one embodiment, input analysis unit 120 may merely “pass through”input data 102 without modification. The output of the ANN, indicatingquantification, such as labels pertaining to the entirety of, orportions of input data 102, may then be provided to loss classifier 140,which may insert the output of the ANN into an electronic database, suchas loss data 142. Alternatively, or additionally, quantification unit154 may use information output by the ANN to determine attributes ofinput data 102, and may provide those attributes to loss classifier 140.

AI platform 104 may further include customer data 160 and life andhealth data 162, which loss classifier 140 may leverage to provideuseful input parameters to ANN unit 150. Customer data 160 may be anintegral part of AI platform 104, or may be located separately from AIplatform 104. In some embodiments, customer data 160 and/or life andhealth data 162 may be provided to AI platform 104 via separate means(e.g., via an API call) and may be accessed by other units or componentsof environment 100. Either may be provided by a third-party service.

Life and health data 162 may be a database comprising informationdescribing various insurance products, and may indicate whether certaininsurance products or policies include certain fields, requirements,capabilities, etc. The capabilities may be listed individually. Both ofcustomer data 160 and/or life and health data 162 may be used to trainan ANN model.

All of the information pertaining to a submitted claim applicant maythen be provided to ANN unit 150, which may—based upon its priortraining on claims from historical data 108—determine that a pluralityof labels apply to the applicant. For example, the labels may includeDECEASED, TERMINAL, SURGERY, ALLERGIES. The labels may have a respectiveconfidence factor, and may be sorted in terms of criticality, and/orgiven pre-assigned weights. The labels and/or weights may be stored inrisk indication data 142, in one embodiment.

In some embodiments, pattern matching unit 128 and natural languageprocessing unit 130 may act in conjunction to determine labels. Forexample, pattern matching unit 128 may include instructions to identifywords indicating a life insurance claim (e.g., “death,” “deceased,”and/or “coroner”). Matched data may be provided to natural languageprocessing unit 130, which may further process the matched data todetermine parts of speech such as verbs and objects, as well asrelationships between the objects. The output of natural languageprocessing unit 130 may be provided to ANN unit 150 and used by trainingunit 152 to train an ANN model to label insurance types. In oneembodiment, ANN unit 150 may be provided with respective labeled claimtype sets (e.g., a first labeled set of life insurance claims, a secondset of health insurance claims, etc.) as training data, to be trained topredict the claim type. The methods and systems described herein mayhelp risk-averse customers to lower their insurance premiums by moreefficiently quantifying settlement amounts. All of the benefits providedby the methods and systems described herein may be realized much morequickly than traditional modeling approaches, with less bias thanapproaches applied by humans.

Exemplary Claim Settlement System

Computing environment 200 is depicted as including a client device 202,a server device 204, and a network 206; however, the aspects describedherein may include any suitable number of such components. Client device202 and server device 204 may communicate via network 206 to collectdata, train and/or operate ANN models, transfer trained ANN models, anddisplay information to a user. FIG. 2 may correspond to one embodimentof environment 100 of FIG. 1 . Client device 202 and/or server device204 may be implemented as any suitable computing device(s) or mobiledevice(s) such as a laptop, smart phone, tablet, server, wearabledevice, smart watch; smart glasses, etc.

Client device 202 may include a memory 208 and a processor 210 forstoring and executing, respectively, a module 212. While referred to inthe singular, processor 210 may include any suitable number ofprocessors of one or more types (e.g., one or more CPUs, graphicsprocessing units (GPUs), cores, etc.). Similarly, memory 208 may includeone or more persistent memories (e.g., a hard drive and/or solid statememory). Although only a single client 202 is depicted in FIG. 2 , itshould be appreciated that it may be advantageous in some embodiments toprovision multiple clients (e.g., thousands or more) for the deploymentand functioning of environment 200.

Module 212, stored in memory 208 as a set of computer-readableinstructions, may be related to an input data collection application 216which, when executed by the processor 210, causes input data to bestored in memory 208. The data stored in memory 208 may correspond to,for example, raw data retrieved from input data 102. Input datacollection application 216 may be implemented as web page (e.g., HTML,JavaScript, CSS, etc.) and/or as a mobile application for use on astandard mobile computing platform.

Input data collection application 216 may store information in memory208, including the instructions required for its execution. While theuser is using input data collection application 216, scripts and otherinstructions comprising input data collection application 216 may berepresented in memory 208 as a web or mobile application. The input datacollected by input data collection application 216 may be stored inmemory 208 and/or transmitted to server device 204 by network interface214 via network 206, where the input data may be processed as describedabove to train an ANN, and/or to collect information pertaining to aninsurance claim and process the claim using the collected informationvia the trained ANN. In one embodiment, input data collectionapplication 216 may be data used to train a model (e.g., scanned claimdata).

Client device 202 may also include GPS sensor 218, an image sensor 220,user input device 222 (e.g., a keyboard, mouse, touchpad, and/or otherinput peripheral device) and display interface 224 (e.g., an screen).User input device 222 may include components that are integral to clientdevice 202, and/or exterior components that are communicatively coupledto client device 202, to enable client device 202 to accept inputs fromthe user. Display 224 may be either integral or external to clientdevice 202, and may employ any suitable display technology. In someembodiments, input device 222 and display 224 are integrated, such as ina touchscreen display. Execution of the module 212 may further cause theprocessor 210 to associate device data collected from client 202 such asa time, date, and/or sensor data (e.g., a camera for photographic orvideo data) with customer data, such as data retrieved from customerdata 160 and life and health data 162, respectively.

A set of information may be obtained from input device 222, and mayinclude information relating to claims previously-filed by the user.Such previously-filed information may be stored in, for example,customer data 272 and may be related to the obtained information usingone or more common identifiers.

In some embodiments, client 202 may receive data from loss data 142 andsettlement offer 106. Such data, indicating predicted settlementamounts, and/or full-fledged binding offers of settlement, may bepresented to a user of client 202 by a display interface 224. The usermay interact with such information via input device 222 and display 224.

Execution of the module 212 may further cause the processor 210 of theclient 202 to communicate with the processor 250 of the server 204 vianetwork interface 214 and network 206. As an example, an applicationrelated to module 212, such as input data collection application 216,may, when executed by processor 210, cause a user interface to bedisplayed to a user of client device 202 via display interface 224. Theapplication may include graphical user input (GUI) components foracquiring data (e.g., a photograph of a death certificate, hospitalbill, etc.) from image sensor 220, GPS coordinate data from GPS sensor218, and textual user input (e.g., the name of a deceased person) fromuser input device(s) 222. The processor 210 may transmit theaforementioned acquired data to server 204, and processor 250 may passthe acquired data to a trained ANN, which may accept the acquired dataand perform a computation (e.g., training of the model, or applicationof the acquired data to a trained ANN model to obtain a result). Withspecific reference to FIG. 1 , the data acquired by client 202 may betransmitted via network 206 to a server implementing AI platform 104,and may be processed by input analysis unit 120 before being applied toa trained ANN by loss classifier 140.

As described with respect to FIG. 1 , the processing of input fromclient 202 may include associating customer data 160 and life data andhealth data 162 with the acquired data. The output of the ANN may betransmitted, by a loss classifier corresponding to loss classifier 140in server 204, to client 202 for display (e.g., in display 224) and/orfor further processing.

Network interface 214 may be configured to facilitate communicationsbetween client 202 and server 204 via any hardwired or wirelesscommunication network, including network 206 which may be a singlecommunication network, or may include multiple communication networks ofone or more types (e.g., one or more wired and/or wireless local areanetworks (LANs) and/or one or more wired and/or wireless wide areanetworks (WANs) such as the Internet). Client 202 may cause claim filingdata to be stored in server 204 memory 252 and/or a remote insurancerelated database such as customer data 160.

Server 204 may include a processor 250 and a memory 252 for executingand storing, respectively, a module 254. Module 254, stored in memory252 as a set of computer-readable instructions, may facilitateapplications related to processing and/or collecting claim filingrelated data, including claim data and claim metadata, and insurancepolicy data. For example, module 254 may include input analysisapplication 260, claim quantification application 262, and ANN trainingapplication 264, in one embodiment.

Input analysis application 260 may correspond to input analysis unit 120of environment 100 of FIG. 1 . Claim quantification application 262 maycorrespond to quantification unit 152 of environment of FIG. 1 , and ANNtraining application 264 may correspond to ANN unit 150 of environment100 of FIG. 1 . Module 254 and the applications contained therein mayinclude instructions which, when executed by processor 250, cause server204 to receive and/or retrieve input data from (e.g., raw data and/or anelectronic claim) from client device 202. In one embodiment, inputanalysis application 260 may process the data from client 202, such asby matching patterns, converting raw text to structured text via naturallanguage processing, by extracting content from images, by convertingspeech to text, and so on.

Throughout the aforementioned processing, processor 250 may read datafrom, and write data to, a location of memory 252 and/or to one or moredatabases associated with server 204. For example, instructions includedin module 254 may cause processor 250 to read data from an historicaldata 270, which may be communicatively coupled to server device 204,either directly or via communication network 206. Historical data 270may correspond to historical data 108, and processor 250 may containinstructions specifying analysis of a series of electronic claimdocuments from historical data 270, as described above with respect toclaims 110-1 through 110-n of historical data 108 in FIG. 1 .

Processor 250 may query customer data 272 and life and health data 274for data related to respective electronic claim documents and raw data,as described with respect to FIG. 1 . In one embodiment, customer data272 and life and health data 274 correspond, respectively, customer data160 and 162. In another embodiment, customer data 272 and/or life andhealth data 274 may not be integral to server 204. Module 254 may alsofacilitate communication between client 202 and server 204 via networkinterface 256 and network 206, in addition to other instructions andfunctions.

Although only a single server 204 is depicted in FIG. 2 , it should beappreciated that it may be advantageous in some embodiments to provisionmultiple servers (e.g., thousands or more) for the deployment andfunctioning of environment 200. For example, the pattern matching unit128 and natural language processing unit 130 of input analysis unit 120may require CPU-intensive processing. Therefore, deploying additionalhardware may provide additional execution speed. Each of historical data270, customer data 272, life and health data 274, and risk indicationdata 76 may be geographically distributed, thus requiring or benefitingfrom more than one server.

While the databases depicted in FIG. 2 are shown as beingcommunicatively coupled to server 204, it should be understood thathistorical claim data 270, for example, may be located within separateremote servers or any other suitable computing devices communicativelycoupled to server 204. Distributed database techniques (e.g., shardingand/or partitioning) may be used to distribute data. In one embodiment,free or open source software such as Apache Hadoop® may be used todistribute data and run applications (e.g., claim quantificationapplication 262). It should also be appreciated that different securityneeds, including those mandated by laws and government regulations, mayin some cases affect the embodiment chosen, and configuration ofservices and components.

In a manner similar to that discussed above in connection with FIG. 1 ,historical claims from historical claim data 270 may be ingested byserver 204 and used by ANN training application 264 to train an ANN.Then, when module 254 processes input from client 202, the data outputby the ANN(s) (e.g., data indicating labels, amounts, weights, etc.) maybe passed to claim quantification application 262 for computation of aclaim amount, which may be expressed in integer, decimal, or any othersuitable format. The predicted claim amount may then be transmitted toclient device 202 and/or another device, in raw numeric form, orinterpolated into a settlement offer (e.g., settlement offer 106). Thepredicted claim settlement amount may be used for further processing byclient device 202, server device 204, or another device.

It should be appreciated that the client/server configuration depictedand described with respect to FIG. 2 is but one possible embodiment. Insome cases, a client device such as client 202 may not be used. In thatcase, input data may be entered—programmatically, or manually—directlyinto device 204. A computer program or human may perform such dataentry. In that case, device may contain additional or fewer components,including input device(s) and/or display device(s). In anotherembodiment, all processing may take place in client 202 and a server 204may not be used. In one embodiment, a client device 202 may be anintegral device to a self-service workstation (e.g., as part of a kioskin an insurance agent location, or in a medical facility).

Weights and claim settlement amounts that may be generated,respectively, during the training and operation of an ANN may appearcounter-intuitive. For example, the weights may appear to be random orpatternless numbers. Predicted claim settlement amounts may appear to betoo high or too low and may require further investigation to identifydecision factors (i.e., reasoning about model weights).

In some embodiments; environmental data and prior related claims may beused to train the ANN. For example, environmental data may include suchinformation as the location in which the claimant lives (e.g., desert,urban, mountainous; etc.). Environmental data may be determined byanalyzing information added to a submitted claim by a claim filer orother individual (e.g., a claims adjuster) and/or information that isobtained by accessing a third-party API, such as a weather API. Priorrelated claims may be claims identified as relating to a particularuser/policyholder.

In operation, the user of client device 202, by operating input device222 and viewing display 224, may open input data collection application216, which depending on the embodiment, may allow the user to enterpersonal information. The user may be an employee of a companycontrolling AI platform 104 or a customer or end user of the company.For example, input data collection application 216 may walk the userthrough the steps of submitting a claim.

Before the user can fully access input data collection application 216,the user may be required to authenticate (e.g., enter a valid usernameand password). The user may then utilize input data collectionapplication 216. Module 212 may contain instructions that identify theuser and cause input data collection application 216 to present aparticular set of questions or prompts for input to the user, based uponany information input data collection application 216 collects,including without limitation information about the user.

In one embodiment, the ANNs used herein to settle claims may be used onan opt-in basis. For example, a user of an insurer web site may bepresented with the option of having a human or trained ANN evaluate afiled claim. As noted, it may be possible to package the trained ANN fordistribution to a client 202 (i.e., the trained. ANN may be operated onthe client 202 without the use of a server 204). In that case, theclaimant may submit a claim and receive an instantaneous settlementoffer. The user may accept the offer even if the user is not connectedto any network, and the user's acceptance may be recorded in the device,such as in memory 208. At a later time, when the device is againconnected to a network, the user's acceptance may be transmitted to aninsurer (e.g., to server device 204 via network 206). At that stage, theinsurer may generate a payment and send the payment to the user.

Module 212 may identify a subset of historical data 270 to be used intraining an ANN, and/or may indicate to server device 204 that the useof a particular ANN model or models is appropriate. For example, if theuser is submitting a health insurance claim, then module 212 maytransmit the user's name and personal information, the location of theuser as provided by GPS 218, a photograph of a bill captured by imagesensor 220, a description of services (e.g., pre-selected from a list ofservices) and other information to server device 204. Any of theinformation known about the claim filer may be presented using variousgraphical user interface controls (e.g., radio boxes, drop-down menus,etc.) which constrain the set of information that the claim filer maysubmit. In some embodiments, free-form information may be allowed to beentered via input device 222.

In some embodiments, location data from client device 202 may be used byan ANN to determine claim settlement amounts. For example, if the useris known to live in Chicago for nine months of the year, and in a ruralarea for three months of the year, then the user/beneficiary may beprovided with an adjusted settlement or installment payment adjustedaccording to a cost-of-living index for the different times of year.

By the time the user of client 202 files a claim, server 204 may havealready processed the electronic claim records in historical data 270and trained an ANN model to analyze the information provided by the userto output claim settlement predictions, labels, and/or weights.

For example, the partner of a 79-year-old recently-deceased individualmay access client 202 by opening an application (e.g., input datacollection application 216). The application may provide the partnerwith the option to submit a life insurance claim under a joint orindividual life insurance policy (e.g., term life, whole life, etc.).The application may provide the partner with the option of selecting thename of the deceased from a menu, and may pre-fill fields with knownpersonal information about the deceased (e.g., name, age, address,telephone number, etc.). The application may allow the partner to selectpre-filled information, and/or to enter their own information.

The application may provide the ability to attach documents (e.g., aphotograph of a death certificate) and may also prompt the claim-filingpartner to select the cause of death from a pre-filled list, or to enterinformation relating to the cause of death. A time/date of death may besolicited. The application may provide the partner with the ability torequest that funeral expenses be paid to a specific mortuary, and suchrequests may be entered separately into the application, and may besubject to expedited review/processing.

The application may allow the claim filer, partner or otherwise, toselect one or more beneficiaries on behalf of the deceased. Then, theclaim filer may have the option of submitting the claim. One or moreANNs may then analyze the inputs provided by the claim filer. Forexample, a first ANN may be applied which analyzes the deathcertificate. Another ANN may analyze the inputs provided by theclaimant. The outputs of the two ANNs may be harmonized. A third ANN maybe selected based upon one or more criteria (e.g., the age of thedeceased, the age of the policy, the status of the policy, etc.). Thethird ANN may analyze the harmonized outputs of the first two ANNs topredict a claim settlement amount. The claim settlement amount may bebased upon claims previously settled for similar life insurance policiesand/or insureds.

Depending upon the result of the ANN analyzing the partner's claim, theapplication may provide the partner with an immediate settlement amount,or may queue the claim for further review. In the case that an immediateamount offer is made, then the application may contain furtherinstructions that will allow the claimant to accept the offer byproviding an e-signature or other manifestation of acceptance.

In some embodiments, a component (e.g., input analysis application 260or a trained ANN) may analyze the claim information and determine thatfurther information is necessary before the claim may be settled. Forexample, a doctor's signature may be required in order for somediagnoses under accelerated death benefits and/or payments under healthinsurance policies to be acceptable. In some embodiments, a particulardata point may cause the claim to be flagged as requiring human review(e.g., if the cause of death is listed as a homicide). In such cases,particular supporting documentation (e.g., police reports) may beautomatically requested from the claimant. It should be appreciated thatother specific constraints may be encoded into the methods and systems.For example, a health insurance claim less that $25 may be subject to aless scrutinizing ANN than a claim over $5000. The claimed amount may,in some cases, be an input provided to the trained ANN, and historicalclaims used in training the ANN may be labeled according to their dollarvalue.

In one embodiment, the death of a policy holder may be automaticallydetected via the monitoring of public records and a claim approved orplaced in condition for approval—subject to beneficiary acceptancethrough automated means. In such embodiments, the information that wouldnormally be provided via the partner in the above example may beextracted from public records. In either case, the information may beanalyzed by the trained ANN to determine an appropriate claim settlementamount. In the automatic detection case, such claim settlement may becommunicated (e.g., mailed, emailed, etc.) to the beneficiary of record,along with information informing the beneficiary of how to effectivelyaccept the settlement.

In embodiments wherein installment payments are made, such as in thecase of accelerated death benefits, an ANN may be used to periodicallyreview a claim of a beneficiary and to adjust benefits accordingly. Forexample, the patient's electronic medical records may be analyzed usingthe ANN via a batch process, and if the patient's condition improves,the benefit may be lowered or may be subject to termination. On theother hand, if the patient's condition worsens, the benefit amount maybe increased correspondingly.

All of the information collected may be associated with a claimidentification number so that it may be referenced as a whole. Server204 may process the information as it arrives, and thus may processinformation collected by input data collection application 216 at adifferent time than server 204 processes the audio recording in theabove example. Once information sufficient to process the claim has beencollected, server 204 may pass all of the processed information (e.g.,from input analysis application) to claim quantification application262, which may apply the information to the trained ANN model.

As noted, more than one trained ANN model may be used to analyze theclaim. Therefore, a first trained ANN model may be used to predict aninstallment payment schedule including respective installment amounts. Asecond trained ANN model may be used to predict a lump sum payment.Quantification unit may provide the results of the first and second ANNmodels to settlement offer 106 in the form of a binary choice to a user,wherein the user may compare the first and second settlement offers andaccept either of the two. Furthermore, each respective choice may have alimited and differing time window within which to accept. Such automatedscheduling may assist the insurer in meeting deadlines. This is animportant benefit of the methods and systems disclosed herein, becausefailure to timely process life insurance claims may cause the insurer tobe subject to penalties in some jurisdictions.

While the claim or application processing is pending, client device 202may display an indication that the processing of the claim is ongoingand/or incomplete. When the claim is ultimately processed by server 204,an indication of completeness may be transmitted to client 202 anddisplayed to user, for example via display 224. Missing information maycause the model to abort with an error.

In some embodiments, the labels and/or characterization of input data(claims and otherwise) performed by the systems and methods describedherein may be capable of dynamic, incremental, and or online training.Specifically, a model that has been trained on a set of electronic claimrecords from historical data 270 may be updated dynamically, such thatthe model may be updated on a much shorter time scale. For example, themodel may be adjusted weekly or monthly to take into accountnewly-settled claims. In one embodiment, the settlement of a claim maytrigger an immediate update of one or more ANN models included in the AIplatform. For example, the settlement of a claim involving a particulartype of cancer may trigger updates to a set of personal injury ANNmodels pertaining to cancer patients. In some embodiments, a humanreviewer or team of reviewers may be responsible for approving thegenerated labels and any associated weightings before they are used.

While FIG. 2 depicts a particular embodiment, the various components ofenvironment 100 may interoperate in a manner that is different from thatdescribed above, and/or the environment 100 may include additionalcomponents not shown in FIG. 2 . For example, an additionalserver/platform may act as an interface between client device 202 andserver device 204, and may perform various operations associated withproviding the labeling and/or risk analysis operations of server 204 toclient device 202 and/or other servers.

Exemplary Artificial Neural Network

FIG. 3 depicts an exemplary artificial neural network (ANN) 300 whichmay be trained by ANN unit 150 of FIG. 2 or ANN training application 264of FIG. 2 , according to one embodiment and scenario. The exemplary ANN300 may include layers of neurons, including input layer 302, one ormore hidden layers 304-1 through 304-n, and an output layer 306. Eachlayer comprising ANN 300 may include any number of neurons—i.e., q and rmay be any positive integers. It should be understood that ANNs may beused to achieve the methods and systems described herein that are of adifferent structure and configuration than those depicted in FIG. 3 .

Input layer 302 may receive different input data. For example, inputlayer 302 may include a first input at which represents an insurancetype (e.g., PPO health insurance), a second input a₂ representingpatterns identified in input data, a third input a₃ representing the ageof the patient, a fourth input a₄ representing the name of the hospitalin which service was rendered, a fifth input a₅ representing whether aclaim was paid or not paid, a sixth input a₆ representing aninflation-adjusted dollar amount claimed by a provider, and so on. Inputlayer 302 may comprise thousands or more inputs. In some embodiments,the number of elements used by ANN 300 may change during the trainingprocess, and some neurons may be bypassed or ignored if, for example,during execution of the ANN, they are determined to be of lessrelevance.

Each neuron in hidden layer(s) 304-1 through 304-n may process one ormore inputs from input layer 302, and/or one or more outputs from aprevious one of the hidden layers, to generate a decision or otheroutput. Output layer 306 may include one or more outputs each indicatinga dollar value, Boolean, and/or weight describing one or more inputs. Alabel may indicate a percentage of the claimed amount (e.g., 85%) or anindication of whether to pay or not (PAY, NO-PAY). In some embodiments,however, outputs of ANN 300 may be obtained from a hidden layer 304-1through 304-n in addition to, or in place of, output(s) from outputlayer(s) 306.

In some embodiments, each layer may have a discrete, recognizable,function with respect to input data. For example, if n=3, a first layermay analyze one dimension of inputs, a second layer a second dimension,and the final layer a third dimension of the inputs, where alldimensions are analyzing a distinct and unrelated aspect of the inputdata. For example, the dimensions may correspond to aspects of a healthinsurance considered strongly determinative, then those that areconsidered of intermediate importance, and finally those that are ofless relevance.

In other embodiments, the layers may not be clearly delineated in termsof the functionality they respectively perform. For example, two or moreof hidden layers 304-1 through 304-n may share decisions relating tosettlement prediction, with no single layer making an independentdecision.

In some embodiments, ANN 300 may be constituted by a recurrent ANN,wherein the calculation performed at each neuron is dependent upon aprevious calculation. It should be appreciated that recurrent ANNs maybe more useful in performing certain tasks, such as predicting theamount to pay to a claimant under claims given the history of otherclaims paid to the claimant. Therefore, in one embodiment, a recurrentANN may be trained with respect to a specific piece of functionalitywith respect to environment 100 of FIG. 1 .

FIG. 4 depicts an example neuron 400 that may correspond to the neuronlabeled as “1,1” in hidden layer 304-1 of FIG. 3 , according to oneembodiment. Each of the inputs to neuron 400 (e.g., the inputscomprising input layer 302) may be weighted, such that input a₁ througha_(p) corresponds to weights w₁ through w_(p), as determined during thetraining process of ANN 300. It should be appreciated that weights maybe very complex sets of variables that may appear non-intuitive whenviewed by humans.

In some embodiments, some inputs may lack an explicit weight, or may beassociated with a weight below a relevant threshold. The weights may beapplied to a function α, which may be a summation and may produce avalue z₁ which may be input to a function 420, labeled as f_(1,1)(z₁).The function 420 may be any suitable linear or non-linear, or sigmoid,function. As depicted in FIG. 4 , the function 420 may produce multipleoutputs, which may be provided to neuron(s) of a subsequent layer, orused directly as an output of ANN 300. For example, the outputs maycorrespond to index values in a dictionary of labels, or may becalculated values used as inputs to subsequent functions.

It should be appreciated that the structure and function of the ANN 300and neuron 400 depicted are for illustration purposes only, and thatother suitable configurations may exist. For example, the output of anygiven neuron may depend not only on values determined by past neurons,but also future neurons. In general, training the neural network modelmay include establishing a network architecture, or topology, and addinglayers that may be associated with one or more activation functions(e.g., a rectified linear unit, softmax, etc.) in addition to lossfunctions and/or optimization functions. Multiple different types ofANNs may be employed, including without limitation, recurrent neuralnetworks, convolutional neural networks, and deep learning neuralnetworks.

Data sets used to train the ANN(s) may be divided into training,validation, and testing subsets; these subsets may be encoded in anN-dimensional tensor, array, matrix, or other suitable data structures.Training may be performed by iteratively training the network usinglabeled training samples. Training of the ANN may produce byproductweights, or parameters which may be initialized to random values. Theweights may be modified as the network is iteratively trained, by usingone of several gradient descent algorithms, to reduce loss and to causethe values output by the network to converge to expected, or “learned”,values.

In one embodiment; a regression neural network may be selected whichlacks an activation function, wherein input data may be normalized bymean centering, to determine loss and quantify the accuracy of outputs.Such normalization may use a mean squared error loss function and meanabsolute error. The ANN model may be validated and cross-validated usingstandard techniques such as hold-out, K-fold, etc. In some embodiments,multiple ANNs may be separately trained and operated, and/or separatelytrained and operated in conjunction.

Exemplary Electronic Claim Processing

The precise manner by which the one or more ANNs employ machine learningto predict claim settlement amounts and/or percentages may differdepending on the content and arrangement of training documents withinthe historical data (e.g., historical data 108 of FIG. 1 and historicaldata 270 of FIG. 2 ) and the input data provided by customers or usersof the AI platform (e.g., input data 102 of FIG. 1 and the datacollected by input data collection application 216 of FIG. 2 ), as wellas the data that is joined to the historical data and input data, suchas customer data 160 of FIG. 1 and customer data 272 of FIG. 2 , andcustomer data 160 of FIG. 1 and life and health data 274 of FIG. 2 .

The initial structure of the ANN(s) (e.g., the number of neuralnetworks, their respective types, number of layers, and neurons perlayer, etc.) may also affect the manner in which the trained neuralnetwork (or other artificial intelligence or machine learning algorithm,program, module, or model) processes the input and claims. Also, asnoted above, the output produced by neural networks may becounter-intuitive and very complex. For illustrative purposes, intuitiveand simplified examples will be discussed in connection with FIG. 5 .

FIG. 5 depicts text-based content of an exemplary electronic claimrecord 500 which may be processed using an ANN, such as ANN 300 of FIG.3 or a different ANN generated/trained by ANN unit 150 of FIG. 1 or ANNtraining application 264 of FIG. 2 . The term “text-based content” asused herein includes printing (e.g., characters A-Z and numerals 0-9) inaddition to non-printing characters (e.g., whitespace, line breaks,formatting, and control characters). Text-based content may be in anysuitable character encoding, such as ASCII or UTF-8 and text-basedcontent may include HTML.

Although text-based-content is depicted in the embodiment of FIG. 5 , asdiscussed above, claim input data may include images, includinghand-written notes, and the AI platform may include an ANN trained torecognize hand-writing and to convert hand-writing to text. Further,“text-based content” may be formatted in any acceptable data format,including structured query language (SQL) tables, flat files,hierarchical data formats (e.g., XML, JSON, etc) or as other suitableelectronic objects. In some embodiments, image and audio data may be feddirectly into the neural network(s) without being converted to textfirst.

With respect to FIG. 5 , electronic claim record 500 includes threesections 510 a-510 c, which respectively represent policy information,loss information, and external information. Policy information 510 a mayinclude information about the insurance policy under which the claim hasbeen made, including the person to whom the policy is issued, the nameof the insured and any additional insureds, the doctor or hospitalsrelated to the insured, mortality, tables, the location of the insured,etc. Policy information 510 a may be read, for example by input analysisunit 120 analyzing historical data, such as historical data 108 andindividual claims, such as claims 110-1 through 110-n.

Additional information about the insured and the circumstances of theclaim may be obtained from data sources and joined to input data. Forexample, additional customer data may be obtained from customer data 160or customer data 272, and additional data may be obtained from life andhealth data 162 and life and health data 274.

In addition to policy information 510 a, electronic claim record 500 mayinclude loss information 510 b. Loss information generally correspondsto information regarding a loss event, such as a medical event, death,accident and/or other peril. As noted, herein “death” for purposes oflife insurance payment may include terminal and/or chronic diagnoses.Loss information 510 b may indicate the date and time of the loss, thetype of loss, whether personal injury occurred, whether the insured madea statement in connection with the loss, whether the loss was settled,and if so for how much money.

In some embodiments; more the than one loss may be represented in lossinformation Slob. For example, a single accident may give rise tomultiple losses under a given policy; for example the simultaneous (ornear-simultaneous) death of two related persons.

In addition to loss information, electronic claim record 500 may includeexternal information 510 c, including but not limited to correspondencewith the policyholder/claimant, statements made by thepolicyholder/claimant, etc. External information 510 c may be textual,audio, or video information. The information may include file namereferences, or may be file handles or addresses that represent links toother files or data sources, such as linked data 520 a-g. It should beappreciated that although only links 520 a-g are shown, more or fewerlinks may be included, in some embodiments.

Electronic claim record 500 may include links to other records,including other electronic claim records. For example, electronic claimrecord 500 may link to notice of loss 520 a, one or more photographs 520b, one or more audio recordings 520 c, one or more investigator'sreports 520 d, one or more forensic reports 520 e, one or more diagrams520 f, and one or more payments 520 g. Data in links 520 a-520 g may beingested by an AI platform such as AI platform 120. For example, asdescribed above, each claim may be ingested and analyzed by inputanalysis unit 120.

AI platform 104 may include instructions which cause input analysis unit120 to retrieve, for each link 520 a-520 g, all available data or asubset thereof. Each link may be processed according to the type of datacontained therein; for example, with respect to FIG. 1 , input analysisunit 120 may process, first, all images from one or more photograph 520b using image processing unit 124. Input analysis unit 120 may processaudio recording 520 c using speech-to-text unit 122.

In some embodiments, a relevance order may be established, andprocessing may be completed according to that order. For example,portions of a claim that are identified as most dispositive of paymentmay be identified and processed first. If, in that example, they aredispositive of payment, then processing of further claim elements may beabated to save processing resources.

Once the various input data comprising electronic claim record 500 hasbeen processed, the results of the processing may, in one embodiment, bepassed to a text analysis unit, and then to neural network (or otherartificial intelligence or machine learning algorithm, program, module,or model). If the AI platform is being trained, then the output of inputanalysis unit 120 may be passed directly to neural network unit 150. Theneurons comprising a first input layer of the neural network beingtrained by neural network unit 150 may be configured so that each neuronreceives particular input(s) which may correspond, in one embodiment, toone or more pieces of information from policy information 510 a, lossinformation Slob, and external information 510 c. The electronic claimrecord 500 may be regressed by one or more neural network.

Similarly, one or more input neurons may be configured to receiveparticular input(s) from links 520 a-520 g. If the AI platform is beingused to accept input to predict a settlement amount during the claimstiling process, then the processing may begin with the use of an inputcollection application, as discussed with respect to one embodiment inFIG. 2 . Some of the links may include references to other related datasets. For example, link 520 g may include a link to beneficiaries underthe policy.

In some embodiments, analysis of input entered by a user may beperformed on a client device, such as client device 202. In that case,output from input analysis may be transmitted to a server, such asserver 204, and may be passed directly as input to neurons of analready-trained neural network, such as a neural network trained byneural network training application 264.

The trained model may be configured so that inputting sample parameters,such as those in the example electronic claim record 500, may accuratelypredict, for example, the estimate of damage ($25,000) and settledamount ($24,500). In this case, random weights may be chosen for allinput parameters. The model may then be provided with training data fromclaims 110-1 through 110-n, which are each pre-processed by thetechniques described herein with respect to FIGS. 1 and 2 to extractindividual input parameters. The electronic claim record 500 may then betested against the model, and the model trained with new training dataclaims, until the predicted dollar values and the correct dollar valuesconverge.

The methods and systems described herein may be capable of analyzingdecades of electronic claim records to build neural network or othermachine learning models, and the formatting of electronic claim recordsmay change significantly from decade to decade, even year to year.Therefore, it is important to recognize that the flexibility built intothe methods and systems described herein allows electronic claim recordsin disparate formats to be consumed and analyzed. Furthermore, FIG. 5depicts a life insurance claim for expository purposes, but in someembodiments other life claim types may be used, such as health insuranceclaims.

Exemplary Computer-Implemented Method

FIG. 6 depicts an example method 600 for handling claim settlements.Method 600 may include receiving a set of labeled historical claims,each one corresponding to a respective adjusted settlement amount (block610). As described above, the historical claims, including life and/orhealth claims, may be labeled according to one or more aspects,including the payout, the type of insurance (e.g., term or whole life,etc.). Method 600 may further include training an ANN using a subset ofthe labeled historical claims and the each respective adjustedsettlement amount (block 620). As described, part of the historicalclaims may be held back for validating the ANN. Method 600 may includereceiving, from a user, a life or health claim (block 630). A life orhealth claim may include a claim under a life insurance policy and/orhealth insurance policy. Method 600 may include analyzing the life orhealth claim using the trained ANN to determine a claim settlementprediction (block 640) and generating, based upon the settlementprediction, a settlement offer (block 650).

Method 600 may also include transmitting the settlement offer to anapplication in a user device (block 660). For instance, the settlementoffer may be texted or transmitted to a user mobile device, such as asmartphone. The user device may be the claimant, a provider (e.g.,hospital), a beneficiary, etc. A “user” for purposes of the methods andsystems herein may be another computer-implemented process, or a human.In some embodiments, a hybrid claim may be submitted, such as in thecase of an accident that included hospitalization and death of a coveredperson. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on drones,vehicles or mobile devices, or associated with smart infrastructure orremote servers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Other types ofdeep, combined, reinforced, or reinforcement learning techniques,programs, models, or modules may also be used.

Machine learning may involve identifying and recognizing patterns inexisting data in order to facilitate making predictions for subsequentdata. For instance, machine learning may involve identifying andrecognizing patterns in existing text or voice/speech data in order tofacilitate making predictions for subsequent data. Voice recognitionand/or word recognition techniques may also be used. Models may becreated based upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as image, mobile device, smart or autonomous vehicle, drone, and/orintelligent home, building, and/or real property telematics data. Themachine learning programs may utilize deep learning algorithms that maybe primarily focused on pattern recognition, and may be trained afterprocessing multiple examples. The machine learning programs may includeBayesian program learning (BPL), voice recognition and synthesis, imageor object recognition, optical character recognition, and/or naturallanguage processing—either individually or in combination. The machinelearning programs may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs.

Additional Considerations

With the foregoing, any users (e.g., insurance customers)′hose data isbeing collected and/or utilized may first opt-in to a rewards, insurancediscount, or other type of program. After the user provides theiraffirmative consent, data may be collected from their medical providerand/or the user's device (e.g., mobile device, smart or autonomousvehicle controller, smart home controller, or other smart devices). Inreturn, the user may be entitled insurance cost savings, includinginsurance discounts for life, health, mobile, renters, personalarticles, and/or other types of insurance.

In other embodiments, deployment and use of neural network models at auser device (e.g., the client 202 of FIG. 2 ) may have the benefit ofremoving any concerns of privacy or anonymity, by removing the need tosend any personal or private data to a remote server (e.g., the server204 of FIG. 2 ).

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement operations or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. These and othervariations, modifications, additions, and improvements fall within thescope of the subject matter herein.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the doings). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium) or hardware. In hardware, the routines,etc., are tangible units capable of performing certain operations andmay be configured or arranged in a certain manner. In exampleembodiments, one or more computer systems (e.g., a standalone, client orserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory product to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory product to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput products, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within an office environment oras a server farm), while in other embodiments the processors may bedistributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within officeenvironment, or a server farm). In other example embodiments, the one ormore processors or processor-implemented modules may be distributedacross a number of geographic locations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for themethods and systems disclosed herein through the principles disclosedherein. Thus, while particular embodiments and applications have beenillustrated and described, it is to be understood that the disclosedembodiments are not limited to the precise construction and componentsdisclosed herein. Various modifications, changes and variations, whichwill be apparent to those skilled in the art, may be made in thearrangement, operation and details of the method and apparatus disclosedherein without departing from the spirit and scope defined in theappended claims.

1-20. (canceled)
 21. A computer-implemented method of claims handling,comprising: receiving a plurality of first artificial neural networksand a second artificial neural network; receiving a life claim, the lifeclaim comprising at least one selected from a group consisting of imagedata and audio data; and analyzing the life claim using the plurality offirst artificial neural networks and the second artificial neuralnetwork to determine a claim settlement prediction by at least:extracting text-based content from the at least one selected from agroup consisting of image data and audio data in the life claim using atleast a natural language processing model; selecting a first artificialneural network from the plurality of first artificial neural networksbased on the extracted text-based content; inputting the extractedtext-based content to the selected first artificial neural network;determining a claim label representing a category of the life claimusing the selected first artificial neural network based at least inpart on the extracted text-based content, the claim label being one of aplurality of predetermined labels; inputting the extracted text-basedcontent and the determined claim label to the second artificial neuralnetwork; and determining the claim settlement prediction using thesecond artificial neural network based at least in part on the extractedtext-based content and the determined claim label.
 22. Thecomputer-implemented method of claim 21, wherein the life claimcorresponds to a life insurance policy.
 23. The computer-implementedmethod of claim 21, wherein the life claim includes a photograph of adeath certificate of a deceased person under an insurance policy relatedto the life claim.
 24. The computer-implemented method of claim 21,wherein the life claim corresponds to one or both of (i) a worker'scompensation insurance policy, and (ii) a disability insurance policy.25. The computer-implemented method of claim 21, wherein the pluralityof first artificial neural networks are trained using a set of labeledhistorical claims, wherein each labeled historical claim in the set oflabeled historical claims corresponds to a respective adjustedsettlement amount and a label, the label being one of the plurality ofpredetermined labels.
 26. The computer-implemented method of claim 25,wherein the adjusted settlement amount is an inflation-adjusted amount.27. The computer-implemented method of claim 21, further comprising:generating, based upon the claim settlement prediction, a settlementoffer; wherein the settlement offer includes one of (i) a lump sumpayment, or (ii) a series of installment payments.
 28. Thecomputer-implemented method of claim 27, further comprising:transmitting the settlement offer to an application in a user device;and displaying, in the user device, the settlement offer.
 29. Thecomputer-implemented method of claim 28, further comprising: receiving,from the user device, a manifestation of acceptance of the settlementoffer.
 30. The computer-implemented method of claim 28, whereindisplaying, in the user device, the settlement offer comprisesdisplaying a binary choice between (i) a lump sum payment, and (ii) aseries of installment payments.
 31. The computer-implemented method ofclaim 28, further comprising: generating, in association with an accountof a beneficiary under an insurance policy associated with the lifeclaim, an automatic payment of money corresponding to the claimsettlement prediction.
 32. A claims handling user device, comprising:one or more processors; one or more memories comprising executableinstructions that, when executed by the one or more processors, causethe one or more processors to: receive a plurality of first artificialneural networks and a second artificial neural network; receive a set oflife claim information from a user device, the set of life claiminformation comprising at least one selected from a group consisting ofimage data and audio data; predict a claim settlement amount byanalyzing the set of life claim information using the plurality of firstartificial neural networks and the second artificial network by atleast: extracting text-based content from the at least one selected froma group consisting of image data and audio data in the set of life claiminformation using at least a natural language processing model;selecting a first artificial neural network from the plurality of firstartificial neural networks based on the extracted text-based content;inputting the extracted text-based content to the selected firstartificial neural network; determining a claim label representing acategory of the set of life claim information using the selected firstartificial neural network based at least in part on the extractedtext-based content, the claim label being one of a plurality ofpredetermined labels; inputting the extracted text-based content and thedetermined claim label to the second artificial neural network; andpredicting the claim settlement amount using the second artificialneural network based at least in part on the extracted text-basedcontent and the determined claim label.
 33. The claims handling userdevice of claim 32, wherein the executable instructions further causethe one or more processors to: generate, based upon the claim settlementamount, a settlement offer; transmit, to the user device, the settlementoffer; and receive, from the user device, a manifestation of acceptance.34. The claims handling user device of claim 32, wherein the executableinstructions further cause the one or more processors to: generate apayment to an account of a beneficiary associated with an insurancepolicy associated with the life claim.
 35. The claims handling userdevice of claim 32, wherein the set of life claim information is a firstset of life claim information, and the application further causes theone or more processors to: receive a second set of life claiminformation; pre-fill a user interface in the user device using thesecond set of life information; and transmit the first set of life claiminformation and the second set of life claim information to a remoteserver.
 36. A non-transitory computer readable medium containingcomputer instructions that, when executed, cause a computer to: receivea plurality of first artificial neural networks and a second artificialneural network; receive a set of life claim information from a device ofa user, the set of life claim information comprising at least oneselected from a group consisting of image data and audio data, predict aclaim settlement amount by analyzing the set of life claim informationusing the plurality of first artificial neural networks and the secondartificial network by at least: extracting text-based content from theat least one selected from a group consisting of image data and audiodata in the set of life claim information using at least a naturallanguage processing model; selecting a first artificial neural networkfrom the plurality of first artificial neural networks based on theextracted text-based content; inputting the extracted text-based contentto the selected first artificial neural network; determining a claimlabel representing a category of the set of life claim information usingthe selected first artificial neural network based at least in part onthe extracted text-based content; inputting the extracted text-basedcontent and the determined claim label to the second artificial neuralnetwork; and predicting the claim settlement amount using the secondartificial neural network based at least in part on the extractedtext-based content and the determined claim label.
 37. Thenon-transitory computer readable medium of claim 36, comprising furthercomputer instructions that, when executed, cause the computer to:generate, based upon the claim settlement amount, a settlement offer;transmit, to the device of the user, the settlement offer; and receive,from the device of the user, a manifestation of acceptance.
 38. Thenon-transitory computer-readable medium of claim 36, wherein theplurality of first artificial neural networks are trained using a set oflabeled historical claims, wherein each labeled historical claim in theset of labeled historical claims corresponds to a respective adjustedsettlement amount and a label, the label being one of a plurality ofpredetermined labels.
 39. The non-transitory computer-readable medium ofclaim 36, comprising further computer instructions that, when executed,cause the computer to: generate a payment to an account of a beneficiaryassociated with an insurance policy associated with the life claim. 40.The non-transitory computer-readable medium of claim 36, wherein thelife claim corresponds to one of (i) life insurance policy, (ii) aworker's compensation insurance policy, or (iii) a disability insurancepolicy.